Dance is a universal human behaviour that is observed particularly in courtship contexts, and that provides information that could be useful to potential partners. Here, we use a data-driven approach to pinpoint the movements that discriminate female dance quality. Using 3D motion-capture we recorded women whilst they danced to a basic rhythm. Video clips of 39 resultant avatars were rated for dance quality, and those ratings were compared to quantitative measurements of the movement patterns using multi-level models. Three types of movement contributed independently to high-quality female dance: greater hip swing, more asymmetric movements of the thighs, and intermediate levels of asymmetric movements of the arms. Hip swing is a trait that identifies female movement, and the ability to move limbs asymmetrically (i.e. independently of the other) may attest to well-developed motor control, so long as this limb independence does not verge into uncontrolled pathological movement. We also found that the same level of dance quality could be predicted by different combinations of dance features. Our work opens avenues to exploring the functional significance, informational content, and temporal sequencing of the different types of movement in dance.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.